Ethnic background is associated with no live kidney donor identified at the time of first transplant assessment—an opportunity missed? A single‐center retrospective cohort study
Bibliographic record
Abstract
Patients from ethnocultural minorities have reduced access to live donor kidney transplant (LDKT). To explore early pretransplant ethnocultural disparities in LDKT readiness, and the impact of the interactions with the transplant program, we assessed if patients had a potential live donor (LD) identified at first pretransplant assessment, and if patients with no LD initially received LDKT subsequently. Single-center, retrospective cohort of adults referred for kidney transplant (KT) assessment. Multivariable logistic regression assessed the association between ethnicity and having a potential LD. Cox proportional hazard analysis assessed the association between no potential LD initially and subsequent LDKT. Of 1617 participants, 66% of Caucasians indicated having a potential LD, compared with 55% of South Asians, 44% of African Canadians, and 41% of East Asians (P < 0.001). In multivariable logistic regression analysis, the odds of having a potential LD identified was significantly lower for African, East and South Asian Canadians. No potential LD at initial KT assessment was associated with lower likelihood of LDKT subsequently (hazard ratio [HR], 0.14; [0.10-0.19]). Compared to Caucasians, African, East and South Asian and African Canadians are less likely to have a potential LD identified at first KT assessment, which predicts a lower likelihood of subsequent LDKT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".